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DAQ-Score Database: assessment of map–model compatibility for protein structure models from cryo-EM maps

Nature Methods volume 20, pages 775–776 (2023)Cite this article

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Cryogenic electron microscopy (cryo-EM) has become a widely used technique in structural biology, and an increasing number of biomolecular structures have been determined and stored in the public databases PDB1 and EMDB2. Despite the popularity and increased rigor, it has been noticed that errors occur in the process of building models from cryo-EM maps more frequently than one might think. Thus, establishing quality assessment methods has become a crucial and urgent task for biomolecular structure determination with cryo-EM3. To address this need, we recently developed a quality assessment method, the Deep-learning-based Amino acid-wise model Quality (DAQ) score, which detects map–model compatibility outliers using deep learning4.

The DAQ-Score Database currently contains over 132,492 protein chain models from 8,705 PDB entries that were derived from cryo-EM maps. We plan to update the database once a month. We targeted protein models from maps at a resolution between 2.5 Å and 5.0 Å, inclusive, since this is the range where the neural network can effectively detect local structural features from a map. Also, we limited the analysis to protein models that have sufficient overlap with their corresponding maps (Supplementary Note 1 and Supplementary Fig. 1). The list of included or excluded maps from the analysis is provided at the database website. In addition to the current version of PDB entries, we have also computed DAQ scores of previous major versions of models if they exist.

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Fig. 1: Overview of data stored in DAQ-Score Database.

Data availability

All precomputed DAQ score data are available at https://daqdb.kiharalab.org/. The DAQ-Score Database will be updated and maintained by the Kihara lab, with possible future assistance from system managers and other structural biology researchers at Purdue University.

Code availability

Source code for computing DAQ scores is available at https://github.com/kiharalab/DAQ.

References

  1. Berman, H. M. et al. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lawson, C. L. et al. Nucleic Acids Res. 44, D396–D403 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Lawson, C. L. et al. Nat. Methods 18, 156–164 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Terashi, G., Wang, X., Maddhuri Venkata Subramaniya, S. R., Tesmer, J. J. G. & Kihara, D. Nat. Methods 19, 1116–1125 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. DAQ-score Google Colab. https://bit.ly/daq-score (accessed 1 May 2023).

  6. DAQ Score software. GitHub https://github.com/kiharalab/DAQ (accessed 1 May 2023).

  7. Sehnal, D. et al. Nucleic Acids Res. 49, W431–W437 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors are grateful to C. Christoffer for proofreading the manuscript. This database is currently financially supported by grants from the US National Institutes of Health (R01GM133840, 3R01GM133840-02S1). D.K. also acknowledges supports from the US National Science Foundation (CMMI1825941, MCB1925643, DBI2146026, IIS2211598, DMS2151678 and DBI2003635). X.W. is a recipient of a MolSSI graduate fellowship.

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Authors and Affiliations

  1. Department of Biological Sciences, Purdue University, West Lafayette, IN, USA

    Tsukasa Nakamura, Genki Terashi & Daisuke Kihara

  2. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Xiao Wang & Daisuke Kihara

Authors
  1. Tsukasa Nakamura
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  2. Xiao Wang
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  3. Genki Terashi
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  4. Daisuke Kihara
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Contributions

D.K. conceived the study. T.N. designed and implemented the DAQ-Score Database. T.N. constructed datasets and T.N. and X.W. performed the computations. T.N., X.W., G.T. and D.K. analyzed the data. T.N. drafted the manuscript and D.K. edited it. All authors read and approved the manuscript.

Corresponding author

Correspondence to Daisuke Kihara.

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The authors declare no competing interests.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2 and Supplementary Figs. 1–3

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Nakamura, T., Wang, X., Terashi, G. et al. DAQ-Score Database: assessment of map–model compatibility for protein structure models from cryo-EM maps. Nat Methods 20, 775–776 (2023). https://doi.org/10.1038/s41592-023-01876-1

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